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Disciplinary Discourse Analytics for Deep Learning

The contemporary focus of bringing higher education to a mass audience exemplified by the emergence of Massive Open Online Courses (MOOCs) and open data initiatives has led to a shift from traditional educational models towards a huge range of flexible options for formal, informal and lifelong learning. However, creating opportunities for deep learning and supporting student engagement in large online or blended classrooms and MOOCs presents a particular challenge. Central to this challenge are the restrictions of current automated assessment techniques and limited opportunity for individualized formative feedback from teachers. In this project, we seek to harness the power of computational techniques to address these limitations. In particular, we focus on discourse-centric analytics to explore the language that students and teachers use as they engage in learning and teaching.

Developing knowledge and competence in any discipline involves developing disciplinary literacy. There are clear theoretical links between teaching, learning and language. Parallels exist between the constructivist theories of Piaget and Vygotsky, the systemic functional linguistics and the learning and language theories of Halliday and the new literacies approach of James Paul Gee. Knowledge in the discipline is both understood and expressed through specific language constructs that differ between experts and novices. Furthermore, these constructs also differ between disciplines. Against this background we frame our study around disciplinary literacy development and ask the broad question: Can the analysis and exposition of the disciplinary discourse of students and teachers inform pedagogical interventions that foster deep learning?

In this project, we have three aims. First, to characterize the language constructs of students and teachers across a number of disciplines. Second, to explore whether understanding disciplinary literacy development can support the provision of effective and formative feedback to learners and the enhance the quality of their skill development. Finally, we address the challenge of automated evaluation of students’ written responses to questions.

These aims address some of the main challenges facing contemporary educational providers and academic institutions and are within the scope of SSHRC identified challenges in the Imagining Canada’s Future initiative.